Detrimental network effects in privacy: A graph-theoretic model for node-based intrusions.
Florimond Houssiau1,2, Piotr Sapieżyński3, Laura Radaelli4
1The Alan Turing Institute, 2QR, John Dodson House, 96 Euston Road, London NW1 2DB, UK.
Patterns (New York, N.Y.)
|January 26, 2023
Summary
We introduce a graph-theoretic model to measure data collection reach and network effects. This framework quantifies how small data breaches can lead to massive information exposure, crucial for data protection laws.
Area of Science:
- Data Science
- Network Analysis
- Information Security
Background:
- Data protection laws emphasize proportionality, yet lack methods to assess modern data collection reach.
- Network effects in data collection remain analytically challenging.
Purpose of the Study:
- To develop a graph-theoretic model for quantifying the reach of networked data collections.
- To introduce metrics for node- and edge-observability.
Main Methods:
- Proposed a graph-theoretic model with node- and edge-observability metrics.
- Derived closed-form expressions for quantifying data collection reach.
- Applied the model to real-world data breach and surveillance scenarios.
Main Results:
- Quantified Cambridge Analytica's data collection from compromised accounts.
- Demonstrated law enforcement's potential communication surveillance from minimal node observation.
- Showcased an app's location monitoring capability through proximity tracing.
Conclusions:
- A robust analytical framework is needed to evaluate data collection proportionality.
- The proposed model effectively quantifies the reach and impact of networked data collection.
- Understanding network effects is vital for assessing data privacy risks.
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